A confusion matrix is a visualization and diagnostic tool typically used to evaluate the effectiveness of a trained classifier, which is a software tool that receives data ultimately belonging to one or more categories as input and predicts it into one of the categories. A classifier is trained with data, the actual category of which is known, and by evaluating the classifier's performance with this known data, the algorithms used by the classifier to predict data is altered to provide optimal performance with future data, the category of which is unknown.
There are disadvantages to the above approach, especially when there are multiple classes acceptable for a prediction. In this case, a classification may be labeled as being in error, when in fact, the classification is acceptable and the classification should not be used as an example of inaccurate performance.
Another disadvantage exists when there are numerous classes and subclasses comprising the confusion matrix, because the display of the confusion matrix may become so large as to be unwieldy to display and use for evaluation. For example, a confusion matrix comprising 1000 rows and 1000 columns would be too large to fit on a typical screen.
Therefore, an approach for generating and displaying a confusion matrix, which does not experience the disadvantages of the above approaches, is desirable. The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.